{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from DSVC.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15., 12.) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DSVC/datasets/cifar-10-batches-py\\data_batch_1\n",
      "DSVC/datasets/cifar-10-batches-py\\data_batch_2\n",
      "DSVC/datasets/cifar-10-batches-py\\test_batch\n",
      "Training data shape:  (20000, 32, 32, 3)\n",
      "Training labels shape:  (20000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'DSVC/datasets/cifar-10-batches-py' # you should change it to your own path, \n",
    "                                                    # or put the dataset to this path\n",
    "\n",
    "\n",
    "# To avoid some memory problem, we load 3 batch of the data(30000 images).\n",
    "# You can change the number '3' to '6' to load the hole dataset(60000 images).\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir, 3)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print ('Training data shape: ', X_train.shape)\n",
    "print ('Training labels shape: ', y_train.shape)\n",
    "print ('Test data shape: ', X_test.shape)\n",
    "print ('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):#return a style of enum,like 0 plane,1 car,2 bird....\n",
    "    idxs = np.flatnonzero(y_train == y) \n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 32, 32, 3)\n",
      "(20000,)\n",
      "(5000, 32, 32, 3)\n",
      "(5000,)\n",
      "(500, 32, 32, 3)\n",
      "(500,)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = range(num_training)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "# flat the image data to a one rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print (X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from DSVC.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `DSVC/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open DSVC/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print ('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 145 / 500 correct => accuracy: 0.290000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print ('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print ('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "  print ('Good! The distance matrices are the same')\n",
    "else:\n",
    "  print ('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "  print('Good! The distance matrices are the same')\n",
    "else:\n",
    "  print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 36.592167 seconds\n",
      "One loop version took 96.259253 seconds\n",
      "No loop version took 0.575461 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "  \"\"\"\n",
    "  Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "  \"\"\"\n",
    "  import time\n",
    "  tic = time.time()\n",
    "  f(*args)\n",
    "  toc = time.time()\n",
    "  return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation.\n",
    "\n",
    "There are three kinds of validation methods([introduction to these methods](http://www.cnblogs.com/zhaohongtian/p/6802327.html)). The method below is S-Folder Cross Validation. If it's difficult for you, use the simple cross-validation alternatively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validate the k is  1\n",
      "validate the k is  3\n",
      "validate the k is  5\n",
      "validate the k is  8\n",
      "validate the k is  10\n",
      "validate the k is  12\n",
      "validate the k is  15\n",
      "validate the k is  20\n",
      "validate the k is  50\n",
      "validate the k is  100\n",
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.257000\n",
      "k = 3, accuracy = 0.263000\n",
      "k = 3, accuracy = 0.273000\n",
      "k = 3, accuracy = 0.282000\n",
      "k = 3, accuracy = 0.270000\n",
      "k = 5, accuracy = 0.265000\n",
      "k = 5, accuracy = 0.275000\n",
      "k = 5, accuracy = 0.295000\n",
      "k = 5, accuracy = 0.298000\n",
      "k = 5, accuracy = 0.284000\n",
      "k = 8, accuracy = 0.272000\n",
      "k = 8, accuracy = 0.295000\n",
      "k = 8, accuracy = 0.284000\n",
      "k = 8, accuracy = 0.298000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 10, accuracy = 0.272000\n",
      "k = 10, accuracy = 0.303000\n",
      "k = 10, accuracy = 0.289000\n",
      "k = 10, accuracy = 0.292000\n",
      "k = 10, accuracy = 0.285000\n",
      "k = 12, accuracy = 0.271000\n",
      "k = 12, accuracy = 0.305000\n",
      "k = 12, accuracy = 0.285000\n",
      "k = 12, accuracy = 0.289000\n",
      "k = 12, accuracy = 0.281000\n",
      "k = 15, accuracy = 0.260000\n",
      "k = 15, accuracy = 0.302000\n",
      "k = 15, accuracy = 0.292000\n",
      "k = 15, accuracy = 0.292000\n",
      "k = 15, accuracy = 0.285000\n",
      "k = 20, accuracy = 0.268000\n",
      "k = 20, accuracy = 0.293000\n",
      "k = 20, accuracy = 0.291000\n",
      "k = 20, accuracy = 0.287000\n",
      "k = 20, accuracy = 0.286000\n",
      "k = 50, accuracy = 0.273000\n",
      "k = 50, accuracy = 0.291000\n",
      "k = 50, accuracy = 0.274000\n",
      "k = 50, accuracy = 0.267000\n",
      "k = 50, accuracy = 0.273000\n",
      "k = 100, accuracy = 0.261000\n",
      "k = 100, accuracy = 0.272000\n",
      "k = 100, accuracy = 0.267000\n",
      "k = 100, accuracy = 0.260000\n",
      "k = 100, accuracy = 0.267000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = []\n",
    "    print(\"validate the k is \", k)\n",
    "    # loop the s fold,select the different train and test data\n",
    "    for s in range(num_folds):\n",
    "        X_train_cv = np.vstack(X_train_folds[:s] + X_train_folds[s+1:]) # 将前s份和s+1份到末尾的矩阵链接\n",
    "        y_train_cv = np.hstack(y_train_folds[:s] + y_train_folds[s+1:]) \n",
    "        X_test_cv = X_train_folds[s] # 测试集为第s份数据\n",
    "        y_test_cv = y_train_folds[s] \n",
    "        # train\n",
    "        classifier.train(X_train_cv, y_train_cv)\n",
    "        # compute the distance\n",
    "        dists = classifier.compute_distances_no_loops(X_test_cv)\n",
    "        # predict \n",
    "        y_pred = classifier.predict_labels(dists, k)\n",
    "        # store the test num \n",
    "        num_test_temp = y_test_cv.shape[0]\n",
    "        # compute the accuracy\n",
    "        pred_accuracy = float(np.sum(y_pred == y_test_cv)) / num_test_temp\n",
    "        k_to_accuracies[k].append(pred_accuracy)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "  accuracies = k_to_accuracies[k]\n",
    "  plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 136 / 500 correct => accuracy: 0.272000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 15\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
